Business intelligence in the electrical power industry

Nowadays, the electrical power industry has gained tremendous interest from both entrepreneurs and researchers due to its essential roles in everyday life. However, the current sources for generating electricity are astonishing decreasing, which leads to more challenges for the power industry. Based on the viewpoint of sustainable development, the solution should maintain three layers of economically, ecologically, and society; simultaneously, support business decision-making, increases organizational productivity and operational energy efficiency. In the smart and innovative technology context, business intelligence solution is considered as a potential option in the data-rich environment, which is still witnessed disjointed theoretical progress. Therefore, this study aimed to conduct a systematic literature review and build a body of knowledge related to business intelligence in the electrical power sector. The author also built an integrative framework displaying linkages between antecedents and outcomes of business intelligence in the electrical power industry. Finally, the paper depicted the underexplored areas of the literature and shed light on the research objectives in terms of theoretical and practical implications

Towards Prescriptive Analytics in Cyber-Physical Systems

More and more of our physical world today is being monitored and controlled by so-called cyber-physical systems (CPSs). These are compositions of networked autonomous cyber and physical agents such as sensors, actuators, computational elements, and humans in the loop. Today, CPSs are still relatively small-scale and very limited compared to CPSs to be witnessed in the future. Future CPSs are expected to be far more complex, large-scale, wide-spread, and mission-critical, and found in a variety of domains such as transportation, medicine, manufacturing, and energy, where they will bring many advantages such as the increased efficiency, sustainability, reliability, and security. To unleash their full potential, CPSs need to be equipped with, among other features, the support for automated planning and control, where computing agents collaboratively and continuously plan and control their actions in an intelligent and well-coordinated manner to secure and optimize a physical process, e.g., electricity flow in the power grid. In today’s CPSs, the control is typically automated, but the planning is solely performed by humans. Unfortunately, it is intractable and infeasible for humans to plan every action in a future CPS due to the complexity, scale, and volatility of a physical process. Due to these properties, the control and planning has to be continuous and automated in future CPSs. Humans may only analyse and tweak the system’s operation using the set of tools supporting prescriptive analytics that allows them (1) to make predictions, (2) to get the suggestions of the most prominent set of actions (decisions) to be taken, and (3) to analyse the implications as if such actions were taken. This thesis considers the planning and control in the context of a large-scale multi-agent CPS. Based on the smart-grid use-case, it presents a so-called PrescriptiveCPS – which is (the conceptual model of) a multi-agent, multi-role, and multi-level CPS automatically and continuously taking and realizing decisions in near real-time and providing (human) users prescriptive analytics tools to analyse and manage the performance of the underlying physical system (or process). Acknowledging the complexity of CPSs, this thesis provides contributions at the following three levels of scale: (1) the level of a (full) PrescriptiveCPS, (2) the level of a single PrescriptiveCPS agent, and (3) the level of a component of a CPS agent software system. At the CPS level, the contributions include the definition of PrescriptiveCPS, according to which it is the system of interacting physical and cyber (sub-)systems. Here, the cyber system consists of hierarchically organized inter-connected agents, collectively managing instances of so-called flexibility, decision, and prescription models, which are short-lived, focus on the future, and represent a capability, an (user’s) intention, and actions to change the behaviour (state) of a physical system, respectively. At the agent level, the contributions include the three-layer architecture of an agent software system, integrating the number of components specially designed or enhanced to support the functionality of PrescriptiveCPS. At the component level, the most of the thesis contribution is provided. The contributions include the description, design, and experimental evaluation of (1) a unified multi-dimensional schema for storing flexibility and prescription models (and related data), (2) techniques to incrementally aggregate flexibility model instances and disaggregate prescription model instances, (3) a database management system (DBMS) with built-in optimization problem solving capability allowing to formulate optimization problems using SQL-like queries and to solve them “inside a database”, (4) a real-time data management architecture for processing instances of flexibility and prescription models under (soft or hard) timing constraints, and (5) a graphical user interface (GUI) to visually analyse the flexibility and prescription model instances. Additionally, the thesis discusses and exemplifies (but provides no evaluations of) (1) domain-specific and in-DBMS generic forecasting techniques allowing to forecast instances of flexibility models based on historical data, and (2) powerful ways to analyse past, current, and future based on so-called hypothetical what-if scenarios and flexibility and prescription model instances stored in a database. Most of the contributions at this level are based on the smart-grid use-case. In summary, the thesis provides (1) the model of a CPS with planning capabilities, (2) the design and experimental evaluation of prescriptive analytics techniques allowing to effectively forecast, aggregate, disaggregate, visualize, and analyse complex models of the physical world, and (3) the use-case from the energy domain, showing how the introduced concepts are applicable in the real world. We believe that all this contribution makes a significant step towards developing planning-capable CPSs in the future.Mehr und mehr wird heute unsere physische Welt überwacht und durch sogenannte Cyber-Physical-Systems (CPS) geregelt. Dies sind Kombinationen von vernetzten autonomen cyber und physischen Agenten wie Sensoren, Aktoren, Rechenelementen und Menschen. Heute sind CPS noch relativ klein und im Vergleich zu CPS der Zukunft sehr begrenzt. Zukünftige CPS werden voraussichtlich weit komplexer, größer, weit verbreiteter und unternehmenskritischer sein sowie in einer Vielzahl von Bereichen wie Transport, Medizin, Fertigung und Energie – in denen sie viele Vorteile wie erhöhte Effizienz, Nachhaltigkeit, Zuverlässigkeit und Sicherheit bringen – anzutreffen sein. Um ihr volles Potenzial entfalten zu können, müssen CPS unter anderem mit der Unterstützung automatisierter Planungs- und Steuerungsfunktionalität ausgestattet sein, so dass Agents ihre Aktionen gemeinsam und kontinuierlich auf intelligente und gut koordinierte Weise planen und kontrollieren können, um einen physischen Prozess wie den Stromfluss im Stromnetz sicherzustellen und zu optimieren. Zwar sind in den heutigen CPS Steuerung und Kontrolle typischerweise automatisiert, aber die Planung wird weiterhin allein von Menschen durchgeführt. Leider ist diese Aufgabe nur schwer zu bewältigen, und es ist für den Menschen schlicht unmöglich, jede Aktion in einem zukünftigen CPS auf Basis der Komplexität, des Umfangs und der Volatilität eines physikalischen Prozesses zu planen. Aufgrund dieser Eigenschaften müssen Steuerung und Planung in CPS der Zukunft kontinuierlich und automatisiert ablaufen. Der Mensch soll sich dabei ganz auf die Analyse und Einflussnahme auf das System mit Hilfe einer Reihe von Werkzeugen konzentrieren können. Derartige Werkzeuge erlauben (1) Vorhersagen, (2) Vorschläge der wichtigsten auszuführenden Aktionen (Entscheidungen) und (3) die Analyse und potentiellen Auswirkungen der zu fällenden Entscheidungen. Diese Arbeit beschäftigt sich mit der Planung und Kontrolle im Rahmen großer Multi-Agent-CPS. Basierend auf dem Smart-Grid als Anwendungsfall wird ein sogenanntes PrescriptiveCPS vorgestellt, welches einem Multi-Agent-, Multi-Role- und Multi-Level-CPS bzw. dessen konzeptionellem Modell entspricht. Diese PrescriptiveCPS treffen und realisieren automatisch und kontinuierlich Entscheidungen in naher Echtzeit und stellen Benutzern (Menschen) Prescriptive-Analytics-Werkzeuge und Verwaltung der Leistung der zugrundeliegenden physischen Systeme bzw. Prozesse zur Verfügung. In Anbetracht der Komplexität von CPS leistet diese Arbeit Beiträge auf folgenden Ebenen: (1) Gesamtsystem eines PrescriptiveCPS, (2) PrescriptiveCPS-Agenten und (3) Komponenten eines CPS-Agent-Software-Systems. Auf CPS-Ebene umfassen die Beiträge die Definition von PrescriptiveCPS als ein System von wechselwirkenden physischen und cyber (Sub-)Systemen. Das Cyber-System besteht hierbei aus hierarchisch organisierten verbundenen Agenten, die zusammen Instanzen sogenannter Flexibility-, Decision- und Prescription-Models verwalten, welche von kurzer Dauer sind, sich auf die Zukunft konzentrieren und Fähigkeiten, Absichten (des Benutzers) und Aktionen darstellen, die das Verhalten des physischen Systems verändern. Auf Agenten-Ebene umfassen die Beiträge die Drei-Ebenen-Architektur eines Agentensoftwaresystems sowie die Integration von Komponenten, die insbesondere zur besseren Unterstützung der Funktionalität von PrescriptiveCPS entwickelt wurden. Der Schwerpunkt dieser Arbeit bilden die Beiträge auf der Komponenten-Ebene, diese umfassen Beschreibung, Design und experimentelle Evaluation (1) eines einheitlichen multidimensionalen Schemas für die Speicherung von Flexibility- and Prescription-Models (und verwandten Daten), (2) der Techniken zur inkrementellen Aggregation von Instanzen eines Flexibilitätsmodells und Disaggregation von Prescription-Models, (3) eines Datenbankmanagementsystem (DBMS) mit integrierter Optimierungskomponente, die es erlaubt, Optimierungsprobleme mit Hilfe von SQL-ähnlichen Anfragen zu formulieren und sie „in einer Datenbank zu lösen“, (4) einer Echtzeit-Datenmanagementarchitektur zur Verarbeitung von Instanzen der Flexibility- and Prescription-Models unter (weichen oder harten) Zeitvorgaben und (5) einer grafische Benutzeroberfläche (GUI) zur Visualisierung und Analyse von Instanzen der Flexibility- and Prescription-Models. Darüber hinaus diskutiert und veranschaulicht diese Arbeit beispielhaft ohne detaillierte Evaluation (1) anwendungsspezifische und im DBMS integrierte Vorhersageverfahren, die die Vorhersage von Instanzen der Flexibility- and Prescription-Models auf Basis historischer Daten ermöglichen, und (2) leistungsfähige Möglichkeiten zur Analyse von Vergangenheit, Gegenwart und Zukunft auf Basis sogenannter hypothetischer „What-if“-Szenarien und der in der Datenbank hinterlegten Instanzen der Flexibility- and Prescription-Models. Die meisten der Beiträge auf dieser Ebene basieren auf dem Smart-Grid-Anwendungsfall. Zusammenfassend befasst sich diese Arbeit mit (1) dem Modell eines CPS mit Planungsfunktionen, (2) dem Design und der experimentellen Evaluierung von Prescriptive-Analytics-Techniken, die eine effektive Vorhersage, Aggregation, Disaggregation, Visualisierung und Analyse komplexer Modelle der physischen Welt ermöglichen und (3) dem Anwendungsfall der Energiedomäne, der zeigt, wie die vorgestellten Konzepte in der Praxis Anwendung finden. Wir glauben, dass diese Beiträge einen wesentlichen Schritt in der zukünftigen Entwicklung planender CPS darstellen.Mere og mere af vores fysiske verden bliver overvåget og kontrolleret af såkaldte cyber-fysiske systemer (CPSer). Disse er sammensætninger af netværksbaserede autonome IT (cyber) og fysiske (physical) agenter, såsom sensorer, aktuatorer, beregningsenheder, og mennesker. I dag er CPSer stadig forholdsvis små og meget begrænsede i forhold til de CPSer vi kan forvente i fremtiden. Fremtidige CPSer forventes at være langt mere komplekse, storstilede, udbredte, og missionskritiske, og vil kunne findes i en række områder såsom transport, medicin, produktion og energi, hvor de vil give mange fordele, såsom øget effektivitet, bæredygtighed, pålidelighed og sikkerhed. For at frigøre CPSernes fulde potentiale, skal de bl.a. udstyres med støtte til automatiseret planlægning og kontrol, hvor beregningsagenter i samspil og løbende planlægger og styrer deres handlinger på en intelligent og velkoordineret måde for at sikre og optimere en fysisk proces, såsom elforsyningen i elnettet. I nuværende CPSer er styringen typisk automatiseret, mens planlægningen udelukkende er foretaget af mennesker. Det er umuligt for mennesker at planlægge hver handling i et fremtidigt CPS på grund af kompleksiteten, skalaen, og omskifteligheden af en fysisk proces. På grund af disse egenskaber, skal kontrol og planlægning være kontinuerlig og automatiseret i fremtidens CPSer. Mennesker kan kun analysere og justere systemets drift ved hjælp af det sæt af værktøjer, der understøtter præskriptive analyser (prescriptive analytics), der giver dem mulighed for (1) at lave forudsigelser, (2) at få forslagene fra de mest fremtrædende sæt handlinger (beslutninger), der skal tages, og (3) at analysere konsekvenserne, hvis sådanne handlinger blev udført. Denne afhandling omhandler planlægning og kontrol i forbindelse med store multi-agent CPSer. Baseret på en smart-grid use case, præsenterer afhandlingen det såkaldte PrescriptiveCPS hvilket er (den konceptuelle model af) et multi-agent, multi-rolle, og multi-level CPS, der automatisk og kontinuerligt tager beslutninger i nær-realtid og leverer (menneskelige) brugere præskriptiveanalyseværktøjer til at analysere og håndtere det underliggende fysiske system (eller proces). I erkendelse af kompleksiteten af CPSer, giver denne afhandling bidrag til følgende tre niveauer: (1) niveauet for et (fuldt) PrescriptiveCPS, (2) niveauet for en enkelt PrescriptiveCPS agent, og (3) niveauet for en komponent af et CPS agent software system. På CPS-niveau, omfatter bidragene definitionen af PrescriptiveCPS, i henhold til hvilken det er det system med interagerende fysiske- og IT- (under-) systemer. Her består IT-systemet af hierarkisk organiserede forbundne agenter der sammen styrer instanser af såkaldte fleksibilitet (flexibility), beslutning (decision) og præskriptive (prescription) modeller, som henholdsvis er kortvarige, fokuserer på fremtiden, og repræsenterer en kapacitet, en (brugers) intention, og måder til at ændre adfærd (tilstand) af et fysisk system. På agentniveau omfatter bidragene en tre-lags arkitektur af et agent software system, der integrerer antallet af komponenter, der er specielt konstrueret eller udbygges til at understøtte funktionaliteten af PrescriptiveCPS. Komponentniveauet er hvor afhandlingen har sit hovedbidrag. Bidragene omfatter beskrivelse, design og eksperimentel evaluering af (1) et samlet multi- dimensionelt skema til at opbevare fleksibilitet og præskriptive modeller (og data), (2) teknikker til trinvis aggregering af fleksibilitet modelinstanser og disaggregering af præskriptive modelinstanser (3) et database management system (DBMS) med indbygget optimeringsproblemløsning (optimization problem solving) der gør det muligt at formulere optimeringsproblemer ved hjælp af SQL-lignende forespørgsler og at løse dem "inde i en database", (4) en realtids data management arkitektur til at behandle instanser af fleksibilitet og præskriptive modeller under (bløde eller hårde) tidsbegrænsninger, og (5) en grafisk brugergrænseflade (GUI) til visuelt at analysere fleksibilitet og præskriptive modelinstanser. Derudover diskuterer og eksemplificerer afhandlingen (men giver ingen evalueringer af) (1) domæne-specifikke og in-DBMS generiske prognosemetoder der gør det muligt at forudsige instanser af fleksibilitet modeller baseret på historiske data, og (2) kraftfulde måder at analysere tidligere-, nutids- og fremtidsbaserede såkaldte hypotetiske hvad-hvis scenarier og fleksibilitet og præskriptive modelinstanser gemt i en database. De fleste af bidragene på dette niveau er baseret på et smart-grid brugsscenarie. Sammenfattende giver afhandlingen (1) modellen for et CPS med planlægningsmulighed, (2) design og eksperimentel evaluering af præskriptive analyse teknikker der gør det muligt effektivt at forudsige, aggregere, disaggregere, visualisere og analysere komplekse modeller af den fysiske verden, og (3) brugsscenariet fra energiområdet, der viser, hvordan de indførte begreber kan anvendes i den virkelige verden. Vi mener, at dette bidrag udgør et betydeligt skridt i retning af at udvikle CPSer til planlægningsbrug i fremtiden

Reengineering and development of IoT Systems for Home Automation

BEng Thesis, Instituto Superior de Engenharia do Porto.With the increasing adoption of technology in today’s houses, electricity is at an all-time high demand. In fact, given the plethora of vital electricity-powered appliances used every day, such as refrigerators, washing machines, and so forth, it has been proven difficult to even handle all devices’ electric consumption. To reduce consumption costs and turn it into a more manageable process, the concept of flex-offers was created. A flex-offer is built around scheduling energy usage in conjunction with the prices of electricity, as provided by an energy market. More specifically, a flex-offer is an energy consumption offer containing the user’s energy consumption flexibility, which is sent to an entity called the Aggregator, who aggregates together flex-offers from multiple parties, bargains with the energy market, and responds to each flex-offer with a schedule that meets the lowest prices for consumption, while still satisfying the users’ needs. By using flex-offers on a house’s equipment, the idea of FlexHousing was born. The aspired goal of the CISTER Research Center’s FlexHousing project is to deliver a platform where users can register their smart appliances, regardless of its brand and distributor, set up preferences for the devices’ usage, and let the system manage the energy consumption and device activation schedules based on the energy market prices. A previous project had already built a prototype of the FlexHousing system. Nevertheless, the original platform had many limitations and lacked maturity from a software engineering point of view, and the goal of this internship is to apply a reengineering process on the FlexHousing project, while also adding new features to it. Thus, the project’s domain model, its database, and class structures were altered to satisfy the new requirements. Furthermore, its web platform was rebuilt from the ground up. Also, a new interface was developed to facilitate support for devices of different brands. As a proof of concept for the benefits provided by this new interface, a connection with a new device (Sonoff Pow) was also established. Moreover, a new functionality was developed to identify a device’s type of appliance based on its energy consumption, in other words, to specify if a device is, for instance, a refrigerator or not. Finally, another new feature was added in which, based on a device’s type and its energy consumption pattern, the flex-offer creation is automated, minimizing user input. As planned, the FlexHousing platform now supports multiple types of devices, and has a software interface to support more types in the future with minimal effort. The flex-offer creation process has been simplified and is now partially automated. Finally, the web platform’s UI has been updated, becoming more intuitive and appealing to the user.info:eu-repo/semantics/publishedVersio

Time Series Management Systems:A Survey

The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD

Location Awareness in Multi-Agent Control of Distributed Energy Resources

The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theoretical analysis and real network data collection, various sources of location data were identified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non-market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain

Aggregation techniques for energy flexibility

Cotutela Universitat Politècnica de Catalunya i Aalborg UniversitetOver the last few years, the cost of energy from renewable resources, such as sunlight and wind, has declined resulting in an increasing use of Renewable Energy Sources (RES). As a result, the energy produced by RES is fed into the power grid while their share is expected to significantly increase in the future. However, RES are characterized by power fluctuations and their integration into the power grid might lead to power quality issues, e.g., imbalances. At the same time, new energy hungry devices such as heat-pumps and Electric Vehicles (EVs) become more and more popular. As a result, their demand in power, especially during peak-times, might lead to electrical grid overloads and congestions. In order to confront the new challenges, the power grid is transformed into the so-called Smart Grid. Major role in Smart Grid plays the Demand Response (DR) concept. According to DR, Smart Grid better matches energy demand and sup- ply by using energy flexibility. Energy flexibility exists in many individual prosumers (producers and/or consumers). For instance, an owner of an EV plugs-in his EV for more time than it is actually needed. Thus, the EV charging can be timely shifted. The load demanded for charging could be moved to time periods when production from wind turbines is high or away from peak-hours. Thus, RES share is increased and/or the electrical grid operation is improved. The Ph.D. project is sponsored by the Danish TotalFlex project (http://totalflex.dk). Main goal of the TotalFlex project is to design and establish a flexibility market framework where flexibility from individual prosumers, e.g., household devices, can be traded among different market actors such as Balance Responsible Parties (BRPs) and distribution system operators. In order for that to be achieved, the TotalFlex project utilizes the flex-offer concept. Based on the flex-offer concept, flexibility from individual prosumers is captured and represented by a generic model. However, the flexible loads from individual prosumers capture very small energy amounts and thus cannot be directly traded in the market. Therefore, aggregation becomes essential. The Ph.D. project focuses on developing aggregation techniques for energy flexibilities that will provide the opportunity to individual prosumers to participate in such a flexibility market. First, the thesis introduces several flexibility measurements in order to quantify the flexibility captured by the flex-offer model and compare flex- offers among each other, both on an individual and on an aggregated level. Flexibility is both the input and the output of the aggregation techniques. Aggregation techniques aggregate energy flexibility to achieve their goals and, at the same time, they try to retain as much flexibility as possible to be traded in the market. Thus, second, the thesis describes base-line flex-offer aggregation techniques and presents balance aggregation techniques that focus on balancing out energy supply and demand. Third, since there are cases where electrical grid congestions occur, the thesis presents two constraint-based aggregation techniques. The techniques efficiently aggregate large amounts of flex-offers taking into account physical constraints of the electrical grid. The produced aggregated flex-offers are still flexible and when scheduled, a normal grid operation is achieved. Finally, the thesis examines the financial benefits of the aggregation techniques. It introduces flex-offer aggregation techniques that take into account real market technical requirements. As a result, individual small flexible loads can be indirectly traded in the energy market through aggregation. The proposed aggregation techniques for energy flexibilities can con- tribute to the use of flexibility in the Smart Grid in both current and future market frameworks. The designed techniques can improve the services offered to the prosumers and avoid the very costly upgrades of the distribution network.Gennem de senere år er prisen faldet på energi fra vedvarende energikilder såsom sollys og vind, hvilket har medført et stigende forbrug af vedvarende energi. Dette har resulteret i, at energi, der produceret af vedvarende energi, sendes ud i elnettet og andelen forventes at stige markant i fremtiden. Vedvarende energi er imidlertid karakteriseret af effektsvingninger, og integrationen i elnettet kan føre til kvalitetsproblemer med strømmen som for eksempel uligevægt. Samtidig bliver enheder, der sluger vedvarende energi såsom varmepumper og elektriske køretøjer, mere og mere populære. Dette resulterer i, at efterspørgslen på energi, især i spidsbelastede situationer, kan medføre overbelastning og trængsel på elnettet. For at konfrontere de nye udfordringer bliver elnettet ændret til et såkaldt Smart Grid. Konceptet om udbud og efterspørgsel Demand Response (DR) spiller her en meget stor rolle. Ifølge DR, imødegår Smart Grid bedre udbud og efterspørgsel af energi ved at bruge fleksibel energi. Fleksibel energi eksisterer i mange individuelle producenter og/eller forbrugere. For eksempel tilslutter en ejer af et elektrisk køretøj sit køretøj i mere tid end det rent faktisk er nødvendigt. På denne måde kan tidspunktet for opladningen ændres rettidigt. Belastningen, der kræves for opladning, kunne flyttes til perioder, hvor produktion fra vindmøller er høj eller væk fra de spidsbelastede tidspunkter. Således øges vedvarende energi’ andel og/eller elnettets drift er forbedret. Dette Ph.D. projekt er sponsoreret af det danske TotalFlex projekt (http://totalflex.dk). TotalFlex’ formål er at designe og etablere et fleksibelt elmarkedsystem, hvor fleksibilitet fra individuelle producent og/ eller forbruger f.eks. husholdningsenheder kan blive udvekslet mellem forskellige markedsaktører såsom balanceansvarlige parter og eldistributionsnettets operatører. For at opnå dette, udnytter TotalFlex flex-offer konceptet. Baseret på konceptet om flex-offer, bliver fleksibilitet fra individuelle prosumers fanget og repræsenteret i en generisk model. Fleksible belastninger fra de individuelle prosumers fanger imidlertid kun meget små energimængder og kan ikke udveksles direkte på markedet. Derfor bliver aggregering essentielt. Ph.D. projektet fokuserer på at udvikle aggregering-steknikker for energifleksibilitet, der kan give individuelle prosumers mulighed for at deltage i et sådant fleksibilitetsmarked. Først vil afhandligen introducere adskillige fleksibilitetsmålinger for at kvantificere fleksibiliteten, der fanges af flex-offer modellen og sammenligne flex-offer med hinanden både på et individuelt og et aggregeret niveau. Input og output af aggregeringsteknikker er fleksibilitet. Aggregeringsteknikker samler energifleksibilitet for at opnå dets mål og forsøger på samme tid at beholde så meget fleksibilitet som muligt til at blive udvekslet på markedet. Herpå forsøger afhandligen for det andet at beskrive basis flexoffer aggregeringsteknikker og præsenterer balance-aggregeringsteknikker, der fokuserer på at afbalancere energiudbud og -efterspørgsel. Siden der er situationer, hvor overbelastninger af elnettet forekommer, præsenterer afhandlingen for det tredje, to begrænsningsbaserede aggregeringsteknikker. Teknikkerne samler effektivt store mængder af flex-offers og tager samtidig hensyn til fysiske begrænsninger i elnettet. De producerede, samlede flexoffers er stadig fleksible og efter det er planlagt, opnås et normaltfungerende net. Til slut vil afhandlingen undersøge de økonomiske fordele ved aggregeringsteknikkerne. Den introducerer flex-offer aggregeringsteknikkerne, der tager højde for de reelle, tekniske krav, der er på markedet. Resultatet kan være, at individuelle små fleksible belastninger indirekte kan udveksles på energimarkedet gennem aggregering. De foreslåede aggregeringsteknikker til energi-fleksibilitet kan bidrage til brug af fleksibilitet i Smart Grid i både nuværende og fremtidige markedsrammer. De designede teknikker kan forbedre de tilbudte ydelser til prosumers og undgå de meget dyre opgraderinger af distributionsnetværkDurante los últimos años, la bajada en el precio de la energía procedente de fuentes renovables, tales como luz solar y eólica, ha resultado en un aumento del uso de este tipo de recursos de Energía Renovables (ER). Como consecuencia de este aumento, la energía producida a través de ER es inyectada en la red eléctrica y se espera que la proporción de energía suministrada a la red crezca significativamente en los próximos años. Sin embargo, las ER se caracterizan por ser muy fluctuantes y su integración en la red eléctrica podría acarrear problemas de calidad, como por ejemplo desequilibrios energéticos. Al mismo tiempo, nuevos dispositivos de alto consumo de energía, como bombas de calor y vehículos eléctricos, son cada vez mas populares y la alta demanda de estos, especialmente en horas puntas, puede crear sobrecargas y congestiones en la red. Para afrontar estos restos, la red eléctrica se transforma en la llamada Red Inteligente, dónde el concepto de respuesta a la demanda juega un papel. Esta thesis de doctorado está patrocinada por el proyecto danés TotalFlex (http://totalflex.dk). El objetivo principal de este proyecto es diseñar y establecer el marco de flexibilidad de mercado, dónde la flexibilidad de productores/consumidores, por ejemplo los dispositivos del hogar, pueda ser comercializada entre los diferentes actores del mercado como las comercializadoras de electricidad y los operadores de sistemas de distribución. Para lograr este propósito, el proyecto TotalFlex utiliza el concepto flex-offer flexibilidad en la oferta. Basado en el concepto flex-offer, la flexibilidad de consumidores y productores individuales es capturada y representada a través de un modelo genérico. Sin embargo, las cargas flexibles de estos individuos producen pequeñas cantidades de energía y, por lo tanto, no pueden ser directamente negociadas en el mercado. Esto significa que la agregación de esta energía es esencial. Este Ph.D está enfocado desarrollo de técnicas de agrega para que permitirán a productores y consumidores individuales participar en dicha. En primer lugar, esta tesis introduce medidas de flexibilidad con la finalidad de cuantificar la flexibilidad calculada por el modelo flex-offer y comparar las diferentes ofertas entre ellas, tanto a nivel individual como agregado. Flexibilidad es tanto la entrada como la salida de las técnicas de agregado, las cuáles agregan flexibilidad energética para lograr sus objetivos y, al mismo tiempo, retener la máxima flexibilidad para comerciarla en el mercado. En segundo lugar, la tesis describe la base de las tecnicas de agregado flex-offer y presenta técnicas de que se enfocan en un balance entre la oferta y la demanda energética. Tercero, dado que existen casos dónde se producen congestiones en la red eléctrica, la tesis presenta tecnicas de agregado basadas en restricciones. Dichas técnicas agregan grandes cantidades de flex-offers considerando restricciones físicas de la red eléctrica. Las flexoffers agregadas que se producen son aún flexibles y, cuando se programan, se logra una operación normal de red. Por último, en la tesis se examina los beneficios económicos de las técnicas agregadas, introduciendo técnicas de agregado flex-offer que tienen en cuenta los requisitos técnicos del mercado real. Como resultado, las pequeñas cargas individuales y flexibles pueden ser indirectamente negociadas en el mercado energético a través de la agregación. Las técnicas de agregado propuestas para favorecer la flexibildad energética puede contribuir al uso de flexibilidad en la red inteligente tanto en el presente como en el futuro. Mejorar los servicios ofrecidos a consumidores y productores así como evitar las costosas actualizaciones de la red de distribución.Postprint (published version

Community description

petersanders2015aThis document constitutes the 3rd revision of Ready4SmartCities’ Community Description onthe plan on how to build a community for the Ready4SmartCities roadmap, vision andoutcome, also in the light of the targeted data interoperability proposals work packages 2, 3and 4 dealt with. It intends to depict the project’s community of communities at the end ofthe project’s lifetime, the different means the project used to get in touch with it and the viewof building a “data community” via semantic web technologies. It recapitulates and criticallyassesses the problems encountered during the execution of the project concerninginteractions and a channel used, and discusses issues arising in the work to fully reach thetargeted audience(s)